Search Results (100)

Accurate statistical inference for zero-inflated and highly skewed data requires confidence interval procedures with a strong finite-sample performance. The delta–inverse Gaussian distribution provides a flexible framework for modeling such data by combining a point mass at zero with an inverse Gaussian distribution for positive observations, making it suitable for application in various fields such as traffic mortality, insurance, and environmental studies. This paper develops and compares several confidence interval estimation methods for the difference and the ratio of two variances from independent delta–IG distributions. The proposed approaches include adjusted generalized confidence intervals, fiducial confidence intervals, Bayesian credible intervals, the method of variance estimates recovery, and normal approximation methods used as benchmarks. The finite-sample performance of these methods is evaluated through Monte Carlo simulations under various parameter configurations and both balanced and unbalanced sample sizes, with an emphasis on coverage probability and interval width. The simulation results show that AGCI and MOVER generally achieve coverage probabilities close to the nominal level while producing relatively narrow intervals. The MOVER performs particularly well when zero-inflation probabilities are equal, whereas AGCI is more effective when they differ. Illustrative real-data examples are provided to demonstrate practical implementations. Full article

Urban recreational lakes require systematic microbiological monitoring to ensure public health protection and agreement with bathing-water regulations. This study investigates the spatial variability in fecal indicator bacteria (Escherichia coli and intestinal enterococci) at two major urban bathing sites in Zagreb, Croatia (Lake Bundek and Lake Jarun), using a four-year monitoring dataset (2016–2019) collected at 19 fixed sampling locations. E. coli was quantified using a miniaturized most probable number (MPN) method, while intestinal enterococci were determined by membrane filtration, following ISO standards. Microbiological concentrations were log₁₀-transformed and analyzed using one-way analysis of variance (ANOVA) to test for statistically significant differences among sampling locations within each lake. Variability in microbiological data was characterized using box-and-whisker plots, which appropriately represent dispersion and skewness typical of MPN- and CFU-based measurements. The results indicate predominantly homogeneous spatial distributions of both indicators, particularly at Lake Jarun, where no statistically significant differences among sampling locations were observed. In contrast, a statistically significant spatial difference in E. coli concentrations was detected at Lake Bundek, likely reflecting site-specific characteristics such as smaller lake size and more limited water exchange. Full article

We develop a unified likelihood-based framework for limited outcomes built on the two-piece normal family. The framework includes a censored specification that accommodates boundary inflation, a doubly truncated specification on $(0,1)$ for rates and proportions, and a survival formulation with a log-two-piece normal baseline and Gamma frailty to account for unobserved heterogeneity. We derive closed-form building blocks (pdf, cdf, survival, hazard, and cumulative hazard), full log-likelihoods with score functions and observed information, and stable reparameterizations that enable routine optimization. Monte Carlo experiments show a small bias and declining RMSE with increasing sample size; censoring primarily inflates the variability of regression coefficients; the scale parameter remains comparatively stable, and the shape parameter is most sensitive under heavy censoring. Applications to HIV-1 RNA with a detection limit, household food expenditure on $(0,1)$, labor-supply hours with a corner solution, and childhood cancer times to hospitalization demonstrate improved fit over Gaussian, skew-normal, and beta benchmarks according to AIC/BIC/CAIC and goodness-of-fit diagnostics, with model-implied censoring closely matching the observed fraction. The proposed formulations are tractable, flexible, and readily implementable with standard software. Full article

The correct estimation of the population variance plays a vital role in the sampling procedure in surveys, especially when simple random sampling techniques are used. In this work, we propose a new generalized statistical inference in order to estimate the population variance using auxiliary information. We can use the relationship between the study variable and the auxiliary variable to construct a novel generalized class of estimators that is better performing in terms of minimum mean squared error (MSE) and has a higher percentage of relative efficiency than the traditional estimators. The proposed methodology is based on the existing methods of inference with the introduction of modifications to cover the known population parameters of additional auxiliary variables, like the mean, the coefficient of variation, skewness, or kurtosis. Theoretical properties such as bias and mean squared error are obtained with regard to the first-order approximation. The performance of the proposed class of estimators is checked by comparing with that of the classical variance estimators in different population conditions based on real-life data sets and a simulation study. The numerical findings have indicated that the suggested class of estimators is more effective compared to classical methods, especially in cases where there is a very high linear correlation between the auxiliary and the study variables. Also, the estimators are robust, as confirmed using various sample sizes and population structures. The research has made a significant contribution to the development of statistical procedures in survey sampling because the practical and efficient tools provided in the study were useful in estimating the variance. The results have been of great importance when applied by researchers and practitioners active in large-scale surveys. Subsequently, in the case of efficient utilization of auxiliary information, it is feasible to have more accurate and cost-effective statistical inference. Full article

Characterizing tight reservoirs is challenging due to the complex pore structure and strong heterogeneity at various scales. Current digital rock physics often struggles to reconcile high-resolution imaging with representative sample sizes, and 3D digital cores are frequently used primarily as visualization tools rather than predictive, computable platforms. Thus, a clear methodological gap persists: high-resolution models typically lack macroscopic geological features, while existing 3D digital models are seldom leveraged for quantitative, predictive analysis. This study, based on a full-diameter core sample of a single lithology (gray-black shale), aims to bridge this gap by developing an integrated workflow to construct a high-fidelity, computable 3D model that connects the micro–nano to the macroscopic scale. The core was scanned using high-resolution X-ray computed tomography (CT) at 0.4 μm resolution. The raw CT images were processed through a dedicated pipeline to mitigate artifacts and noise, followed by segmentation using Otsu’s algorithm and region-growing techniques in Avizo 9.0 to isolate minerals, pores, and the matrix. The segmented model was converted into an unstructured tetrahedral finite element mesh within ANSYS 2024 Workbench, with quality control (aspect ratio ≤ 3; skewness ≤ 0.4), enabling mechanical property assignment and simulation. The digital core model was rigorously validated against physical laboratory measurements, showing excellent agreement with relative errors below 5% for key properties, including porosity (4.52% vs. 4.615%), permeability (0.0186 mD vs. 0.0192 mD), and elastic modulus (38.2 GPa vs. 39.5 GPa). Pore network analysis quantified the poor connectivity of the tight reservoir, revealing an average coordination number of 2.8 and a pore throat radius distribution of 0.05–0.32 μm. The presented workflow successfully creates a quantitatively validated “digital twin” of a full-diameter core. It provides a tangible solution to the scale-representativeness trade-off and transitions digital core analysis from a visualization tool to a computable platform for predicting key reservoir properties, such as permeability and elastic modulus, through numerical simulation, offering a robust technical means for the accurate evaluation of tight reservoirs. Full article

Background/Objectives: Radiomics, the high-throughput extraction of quantitative features from medical imaging, offers a promising method for identifying laryngeal cancer imaging biomarkers. We aim to systematically review the literature on radiomics in laryngeal squamous cell carcinoma, assessing applications in tumour staging, prognosis, recurrence prediction, and treatment response evaluation. PROSPERO ID: CRD420251117983. Methods: MEDLINE and EMBASE databases were searched in May 2025. Inclusion criteria: studies published between 1 January 2010 and 31 January 2024, extracted radiomic features from CT, PET/CT, or MRI, and analysed outcomes related to diagnosis, staging, survival, recurrence, or treatment response in laryngeal cancer. Exclusion criteria: case reports, abstracts, editorials, reviews, or conference proceedings, exclusive focus on preclinical or animal models, lack of a clear radiomics methodology, or did not include imaging-based feature extraction. Results were synthesised narratively by modelling objective, alongside formal assessment of methodological quality using the Radiomics Quality Score (RQS). Results: Twenty studies met the inclusion criteria, with most using CT-based radiomics. Seven incorporated PET/CT. Radiomic models demonstrated moderate-to-high accuracy across tasks including T-staging, thyroid cartilage invasion, survival prediction, and local failure. Key predictive features included first-order entropy, skewness, and texture metrics such as size zone non-uniformity and GLCM correlation. Methodological variability, limited external validation, and small samples were frequent limitations. Conclusions: Radiomics holds strong promise as a non-invasive biomarker for laryngeal cancer. However, methodological heterogeneity identified through formal quality assessment indicates that improved standardisation, reproducibility, and multicentre validation are required before widespread clinical implementation. Full article

From a psychoneuroimmunology standpoint, stress and cigarette smoking are plausible modulators of periodontal inflammation through neuroendocrine–immune pathways and cytokine networks. Interleukin-18 (IL-1 family), interleukin-21 (common γ-chain cytokine), and interleukin-15 (tissue-resident lymphocyte activation/homeostasis) are mechanistically relevant candidates to characterize in relation to these exposures. We aimed to quantify serum IL-15, IL-18, and IL-21 and examine their associations with stress, smoking, and periodontal status in Mexican adults. Methods: Cross-sectional study (n = 65; 18–60 years; 70.8% female). Smoking status (23.1% smokers) and periodontal status were recorded; due to low periodontitis frequency (n = 3), periodontal status was analyzed as healthy (23.1%) versus periodontal disease (76.9%; gingivitis + periodontitis). Stress was assessed using the 18-item Symptomatic Stress Questionnaire and dichotomized as no/low stress (0–10; 52.3%) versus pathological stress (11–54; 47.7%). Systolic and diastolic blood pressure were recorded. IL-15, IL-18, and IL-21 were measured in serum by immunoassay. Analyses used medians (IQR), Mann–Whitney U tests with rank-biserial effect sizes, and exploratory Benjamini–Hochberg false discovery rate (FDR) adjustment across the nine primary cytokine-by-contrast tests; correlations with age and diastolic blood pressure were exploratory. Results: Cytokine distributions were right-skewed, particularly for IL-21. Across smoking, stress, and periodontal-status contrasts, no comparison met q < 0.05 after FDR adjustment. Effect-size patterns were heterogeneous rather than uniformly monotonic across exposures (e.g., IL-18 showed higher central tendency in healthy vs. periodontal disease; IL-21 showed higher central tendency in no/low stress vs. pathological stress), indicating substantial inter-individual variability in circulating cytokines within this cohort. Conclusions: In this exploratory cross-sectional sample, serum IL-15, IL-18, and IL-21 did not show robust, multiplicity-resistant differences by smoking, stress, or periodontal status. The findings provide a transparent description of distributional properties and hypothesis-generating patterns that motivate larger, longitudinal studies with repeated cytokine sampling, standardized periodontal assessment, and improved control of key confounders to clarify the relevance of these cytokines to periodontal inflammation under behavioral exposures. Full article

Background/Objectives: Since October 2019, Lebanon has faced continuous sociopolitical and economic instability. Clinical psychologists have played a central role in responding to rising mental health needs, yet little is known about their own psychological well-being. Methods: This study examined mental health outcomes among 157 certified psychologists (clinical and educational psychologists) working in Lebanon. A cross-sectional study was conducted with psychologists aged 30–53 years across all Lebanese governorates, who were recruited through snowball and word-of-mouth sampling. Participants completed validated measures of depression (PHQ-9), anxiety (LAS-10), perceived stress (PSS-10), subjective well-being (WHO-5), eating attitudes (EAT-26), and self-esteem (A-SISE). Results: Results showed that 44% of participants reported at least mild depressive symptoms, 14% met criteria for anxiety, and 57% experienced moderate to high perceived stress, while most showed no risk for eating disorders. Bivariate and multivariate analyses identified self-esteem as a predictive factor, negatively associated with depression, anxiety, and stress, and positively associated with subjective well-being. Additional risk factors included younger age, being unmarried, not having children, prior psychological history, health problems, lower income, and working as an educational rather than clinical psychologist. Conclusions: These findings highlight aspects of vulnerability among psychologists and underline the need for targeted interventions for at-risk groups. Strengthening self-esteem may contribute to enhancing clinicians’ mental health. However, these conclusions should be interpreted in light of several limitations, including the small sample size, the non-probability and gender-skewed nature of the sample, partly due to the relatively limited number of practicing psychologists in Lebanon. Full article

Zirconium and its alloys are widely used in nuclear power engineering due to their favorable physical and mechanical properties and their low thermal-neutron absorption cross-section. Their high corrosion resistance in aqueous and steam environments at elevated temperatures is essential for the reliable operation of fuel assemblies and is associated with the formation of a stable, compact ZrO₂ oxide layer. However, under reactor conditions, the presence of hydrogen, iodine and other fission products can reduce corrosion resistance, making detailed corrosion assessment necessary. Manufacturing technology, alongside alloy composition, also plays a decisive role in determining corrosion behavior. This study presents corrosion test results for a Zr-1%Nb alloy processed under thermomechanical conditions corresponding to rolling in a special type of three-roll skew rolling–Radial-Shear Rolling (RSR). The applied rolling technology ensured the formation of a pronounced ultrafine-grained (UFG) structure in the near-surface layers, with an average grain size below 0.6 µm. EBSD and TEM observations revealed a largely equiaxed microstructure with refined grains and increased grain boundary density. The corrosion testing was performed in high-temperature steam vessels at 400 °C and 10.3 MPa for 72, 336, 720 and 1440 h. The results demonstrate that RSR processing is an efficient alternative to conventional multi-pass normal bar rolling with vacuum heat treatments, allowing a significant reduction in processing steps and eliminating the need for expensive tooling and intermediate thermal or chemical treatments. Bars manufactured using this method meet the ASTM B351 requirements. The specific weight gain did not exceed 22 mg/dm² after 72 h and 34.5 mg/dm² after 336 h. After 1440 h, the samples exhibited a continuous, uniform dark-grey oxide layer with an average thickness below 5.3 µm. Full article

Construction and demolition (C&D) waste remains a critical challenge in India due to accelerated urbanisation and material-intensive construction practices. This study integrates survey-based assessment with machine learning to identify key causes of C&D waste and recommend targeted minimization strategies. Data were collected from 116 professionals representing junior, middle, and senior management, spanning age groups from 20 to 60+ years, and working across building construction, consultancy, project management, roadworks, bridges, and industrial structures. The majority of respondents (57%) had 6–20 years of experience, ensuring representation from both operational and decision-making roles. The Relative Importance Index (RII) method was applied to rank waste causes and minimization techniques based on industry perceptions. To enhance robustness, Random Forest, Gradient Boosting, and Linear Regression models were tested, with Random Forest performing best (R² = 0.62), providing insights into the relative importance of different strategies. Findings show that human skill and quality control are most critical in reducing waste across concrete, mortar, bricks, steel, and tiles, while proper planning is key for excavated soil and quality sourcing for wood. Recommended strategies include workforce training, strict quality checks, improved planning, and prefabrication. The integration of perception-based analysis with machine learning offers a comprehensive framework for minimising C&D waste, supporting cost reduction and sustainability in construction projects. The major limitation of this study is its reliance on self-reported survey data, which may be influenced by subjectivity and regional bias. Additionally, results may not fully generalize beyond the Indian construction context due to the sample size and sectoral skew. The absence of real-time site data and limited access to integrated waste management systems also restrict predictive accuracy of the machine learning models. Nevertheless, combining industry perception with robust data-driven techniques provides a valuable framework for supporting sustainable construction management. Full article

Morpho butterflies have the potential to act as a bioindicator species, since they are sensitive to changes in land uses and forest degradation; there have been few population studies of butterflies in the Caribbean region of Colombia. In this study, we aimed to analyze and characterize the population structure of M. helenor peleides and evaluate its variation across different land uses. We used the capture-mark and release technique, with 50 VanSomeren–Rydon type traps distributed in five sampling units with different land uses (forest, restoration areas, and pasture areas); temperature and luminosity were also measured. Butterflies were wing-marked and subsequently released. Sampling was carried out between June and September 2023. Jolly–Seber population models were constructed using the R code to obtain population size (N_i), survival rate, and recruitment; also, other parameters were analyzed (abundance of the imagines, average sex ratio-ASR, displacement, permanence, and age). A total of 876 butterflies were tagged and released, and 33.7% were recaptured. Butterfly abundance was concentrated in conserved sampling units in the forest. The N_i ranged from 25 to 845 individuals within the population, and individual displacement and permanence were restricted in the forest, ASR was significantly male-skewed. The land use directly influenced the population structure of M. helenor peleides, suggesting that conserved areas are key to population persistence. Full article

This study assessed whether permitting certified recycled aggregate companies to assign both quality and environmental management responsibilities to a single individual affects the effectiveness of post-certification quality management. Using data from 242 post-certification audits conducted in 2023, six regulatory audit items were quantified using a binary scoring scheme to produce a six-point score for each company. Audit outcomes were compared between companies employing dedicated quality managers (n = 147) and those operating with concurrently appointed managers (n = 95). Before conducting hypothesis testing, skewness, kurtosis, and F-tests were used to verify approximate normality and homogeneity of variances. Two-sample t-tests assuming equal variances revealed no statistically significant differences between the two personnel structures, and the effect size (Cohen’s d = 0.072) indicated negligible practical differences. Additionally, 52 companies (22%) experienced changes in their quality management personnel during the audit period. A separate comparison between companies with and without such changes also showed no statistically significant differences, with a small effect size (d = 0.276). These results suggest that the 2022 regulatory revision authorizing concurrent appointments did not exert any discernible adverse influence on post-certification audit performance and that additional administrative requirements for managing personnel changes may be unnecessary. The findings also highlight recurring deficiencies—particularly in quality testing and equipment management—which warrant continued attention from policymakers, certification bodies, and certified companies seeking to enhance the effectiveness of the recycled aggregate quality certification system. Full article

This paper introduces the Marshall–Olkin unit-exponentiated-half-logistic (MO-UEHL) distribution, a novel three-parameter model designed to enhance the flexibility of the unit-exponentiated-half-logistic distribution through the incorporation of the Marshall–Olkin transformation. Defined on the unit interval $(0,1)$, the MO-UEHL distribution is well-suited for modeling proportional data exhibiting asymmetry. The Marshall–Olkin tilt parameter $\alpha$ explicitly controls the degree and direction of asymmetry, enabling the density to range from highly right-skewed to nearly symmetric unimodal forms, and even to left-skewed configurations for certain parameter values, thereby offering a direct mathematical representation of symmetry breaking in bounded proportional data. The resulting model achieves this versatility without relying on exponential terms or special functions, thus simplifying computational procedures. We derive its key mathematical properties, including the probability density function, cumulative distribution function, survival function, hazard rate function, quantile function, moments, and information-theoretic measures such as the Shannon and residual entropy. Parameter estimation is explored using maximum likelihood, maximum product spacing, ordinary and weighted least-squares, and Cramér–von Mises methods, with simulation studies evaluating their performance across varying sample sizes and parameter sets. The practical utility of the MO-UEHL distribution is demonstrated through applications to four real datasets from environmental and engineering contexts. The results highlight the MO-UEHL distribution’s potential as a valuable tool in reliability analysis, environmental modeling, and related fields. Full article

Greater amberjack (Seriola dumerili) are large reef fish important in fisheries in the southeastern USA, with the Gulf of America stock unsustainably harvested over most of the past two decades. Its age-based stock assessment and recovery plan depend on age and growth information. In this study, 7658 greater amberjack were sampled from the west coast of Florida and off Alabama, Mississippi, and Louisiana in the Gulf from 1991 to 2018. Fish were aged using cross-sectioned sagittal otoliths, with accompanying data on their length, sex, location (state), and type of fishery. Overall, the greater amberjack that were landed in the recreational and commercial fisheries were between 2 and 19 years of age, with the majority between 3 and 5 years old (>80%), and were primarily caught using hook-and-line gear (95%). Sex- and state-specific growth differences were evident based on von Bertalanffy growth models, with females significantly larger at age than males in both Florida and Louisiana (which included Mississippi and Alabama due to low sample size), and females in Louisiana larger at age than females in Florida. Sex ratios in the recreational catches of Florida and Louisiana were skewed towards females (>1.5 female per male), especially for fish ≥1000 mm fork length (>2.3 female per male). Accounting for sex-specific and region-specific growth differences may, in part, help to explain the notably high variability in the overall growth model for greater amberjack in the Gulf. Full article

This paper introduces the UG-EM (Unconditional Gamma-Exponential Model) as a new compound lifetime model designed to enhance flexibility in tail behavior compared to traditional distributions. The UG-EM model provides a unified framework for analyzing deviations from symmetry in survival data, effectively capturing right-skewed patterns, which are commonly observed in real-world lifetime phenomena. The main analytical properties are derived, including the probability density, cumulative distribution, hazard and reversed-hazard functions, mean residual life, and several measures of dispersion and uncertainty. The effects of the UG-EM parameters (α and λ) are examined, showing that increasing either parameter can cause a temporary reduction in entropy H(T) at early times followed by a long-term increase; in some cases, the influence of α is stronger than that of λ. Parameter estimation is carried out using the maximum likelihood method and assessed through Monte Carlo simulations to evaluate estimator bias and variability, highlighting the significant role of sample size in estimation accuracy. The proposed model is applied to three survival datasets (Lung, Veteran, and Kidney) and compared with classical alternatives such as Exponential, Weibull, and Log-normal distributions using standard goodness-of-fit criteria. Results indicate that the UG-EM model offers superior flexibility and can capture patterns that simpler models fail to represent, although the empirical results do not demonstrate a clear, consistent superiority over standard competitors across all tested datasets. The paper also discusses identifiability issues, estimation challenges, and practical implications for reliability and medical survival analysis. Recommendations for further theoretical development and broader model comparison are provided. Full article